Update app.py

app.py

@@ -1,27 +1,32 @@
-from transformers import pipeline
 import gradio as gr
+from transformers import pipeline
+from PIL import Image
 
-# Ganti model dengan yang khusus AI detection
-classifier = pipeline("image-classification", model="microsoft/ai-image-detector")
+# Gunakan model deteksi publik
+classifier = pipeline("image-classification", model="dima806/deepfake_vs_real_image_detection")
 
-def detect_image(image):
+def detect(image):
+    # image dari Gradio
     results = classifier(image)
-    label = results[0]["label"]
-    score = results[0]["score"] * 100
+    # hasil tertinggi
+    best = max(results, key=lambda x: x['score'])
+    label = best['label']
+    confidence = best['score'] * 100
 
-    if "fake" in label.lower() or "ai" in label.lower():
-        verdict = "Kemungkinan besar Hasil AI"
+    if "fake" in label.lower() or "generated" in label.lower() or "ai" in label.lower():
+        verdict = "⚠️ Kemungkinan besar Gambar AI / Fake"
     else:
-        verdict = "Kemungkinan besar Foto Asli"
+        verdict = "✅ Kemungkinan besar Foto Asli"
 
-    return f"{verdict}\n\nLabel: {label}\nConfidence: {score:.2f}%"
+    return f"{verdict}\n\nLabel: {label}\nConfidence: {confidence:.2f}%"
 
-iface = gr.Interface(
-    fn=detect_image,
-    inputs=gr.Image(type="pil"),
-    outputs="text",
-    title="Deteksi Foto AI vs Asli",
-    description="Upload foto untuk mendeteksi apakah asli atau hasil AI."
+app = gr.Interface(
+    fn=detect,
+    inputs=gr.Image(type="pil", label="Upload Foto"),
+    outputs=gr.Textbox(label="Hasil Deteksi"),
+    title="Detektor Foto vs AI",
+    description="Unggah foto realistik untuk mendeteksi apakah foto asli atau hasil AI."
 )
 
-iface.launch()
+if __name__ == "__main__":
+    app.launch()